Nanovid tracking: a new automatic method for the study of mobility in living cells based on colloidal gold and video microscopy

Quantification of cell shape, intracellular flows and transport based on DIC object detection and tracking

Computational image analysis combined with label-free imaging has helped maintain its relevance for cell biology, despite the rapid technical improvements in fluorescence microscopy with the molecular specificity of tags. Here, we discuss some computational tools developed in our lab and their application to quantify cell shape, intracellular organelle movement and bead transport in vitro, using differential interference contrast (DIC) microscopy data as inputs. The focus of these methods is image filtering to enhance image gradients, and combining them with segmentation and single particle tracking (SPT). We demonstrate the application of these methods to Escherichia coli cell length estimation and tracking of densely packed lipid granules in Caenorhabditis elegans one-celled embryos, diffusing beads in solutions of different viscosities and kinesin-driven transport on microtubules. These approaches demonstrate how improvements to low-level image analysis methods can help obtain insights through quantitative cellular and subcellular microscopy.

Analysis of Phase Heterogeneity in Lipid Membranes Using Single-Molecule Tracking in Live Cells

In live cells, the plasma membrane is composed of lipid domains separated by hundreds of nanometers in dynamic equilibrium. Lipid phase separation regulates the trafficking and spatiotemporal organization of membrane molecules that promote signal transduction. However, visualizing domains with adequate spatiotemporal accuracy remains challenging because of their subdiffraction limit size and highly dynamic properties. Here, we present a single lipid-molecular motion analysis pipeline (lipid-MAP) for analyzing the phase heterogeneity of lipid membranes by detecting the instantaneous velocity change of a single lipid molecule using the excellent optical properties of nanoparticles, high spatial localization accuracy of single-molecule localization microscopy, and separation capability of the diffusion state of the hidden Markov model algorithm. Using lipid-MAP, individual lipid molecules were found to be in dynamic equilibrium between two statistically distinguishable phases, leading to the formation of small (∼170 nm), viscous (2.5× more viscous than surrounding areas), and transient domains in live cells. Moreover, our findings provide an understanding of how membrane compositional changes, i.e., cholesterol and phospholipids, affect domain formation. This imaging method can contribute to an improved understanding of spatiotemporal-controlled membrane dynamics at the molecular level.

Using single molecule imaging to explore intracellular heterogeneity

Despite more than 100 years of study, it is unclear if the movement of proteins inside the cell is best described as a mosh pit or an exquisitely choreographed dance. Recent studies suggest the latter. Local interactions induce molecular condensates such as liquid-liquid phase separations (LLPSs) or non-liquid, functionally significant molecular aggregates, including synaptic densities, nucleoli, and Amyloid fibrils. Molecular condensates trigger intracellular signaling and drive processes ranging from gene expression to cell division. However, the descriptions of condensates tend to be qualitative and correlative. Here, we indicate how single-molecule imaging and analyses can be applied to quantify condensates. We discuss the pros and cons of different techniques for measuring differences between transient molecular behaviors inside and outside condensates. Finally, we offer suggestions for how imaging and analyses from different time and space regimes can be combined to identify molecular behaviors indicative of condensates within the dynamic high-density intracellular environment.

Using Single Molecule Imaging to Explore Intracellular Heterogeneity

Despite more than 100 years of study, it is unclear if the movement of proteins inside the cell is best described as a mosh pit or an exquisitely choreographed dance. Recent studies suggest the latter. Local interactions induce molecular condensates such as liquid-liquid phase separations (LLPSs) or non-liquid, functionally significant molecular aggregates, including synaptic densities, nucleoli, and Amyloid fibrils. Molecular condensates trigger intracellular signaling and drive processes ranging from gene expression to cell division. However, the descriptions of condensates tend to be qualitative and correlative. Here, we indicate how single-molecule imaging and analyses can be applied to quantify condensates. We discuss the pros and cons of different techniques for measuring differences between transient molecular behaviors inside and outside condensates. Finally, we offer suggestions for how imaging and analyses from different time and space regimes can be combined to identify molecular behaviors indicative of condensates within the dynamic high-density intracellular environment.

The Utility of Fluorescence Recovery after Photobleaching (FRAP) to Study the Plasma Membrane

The plasma membrane of mammalian cells is involved in a wide variety of cellular processes, including, but not limited to, endocytosis and exocytosis, adhesion and migration, and signaling. The regulation of these processes requires the plasma membrane to be highly organized and dynamic. Much of the plasma membrane organization exists at temporal and spatial scales that cannot be directly observed with fluorescence microscopy. Therefore, approaches that report on the membrane's physical parameters must often be utilized to infer membrane organization. As discussed here, diffusion measurements are one such approach that has allowed researchers to understand the subresolution organization of the plasma membrane. Fluorescence recovery after photobleaching (or FRAP) is the most widely accessible method for measuring diffusion in a living cell and has proven to be a powerful tool in cell biology research. Here, we discuss the theoretical underpinnings that allow diffusion measurements to be used in elucidating the organization of the plasma membrane. We also discuss the basic FRAP methodology and the mathematical approaches for deriving quantitative measurements from FRAP recovery curves. FRAP is one of many methods used to measure diffusion in live cell membranes; thus, we compare FRAP with two other popular methods: fluorescence correlation microscopy and single-particle tracking. Lastly, we discuss various plasma membrane organization models developed and tested using diffusion measurements.

Single-Molecule Imaging of Membrane Proteins on Vascular Endothelial Cells

Transporting substances such as gases, nutrients, waste, and cells is the primary function of blood vessels. Vascular cells use membrane proteins to perform crucial endothelial functions, including molecular transport, immune cell infiltration, and angiogenesis. A thorough understanding of these membrane receptors from a clinical perspective is warranted to gain insights into the pathogenesis of vascular diseases and to develop effective methods for drug delivery through the vascular endothelium. This review summarizes state-of-the-art single-molecule imaging techniques, such as super-resolution microscopy, single-molecule tracking, and protein-protein interaction analysis, for observing and studying membrane proteins. Furthermore, recent single-molecule studies of membrane proteins such as cadherins, integrins, caveolins, transferrin receptors, vesicle-associated protein-1, and vascular endothelial growth factor receptor are discussed.

Single-shot self-supervised object detection in microscopy

Object detection is a fundamental task in digital microscopy, where machine learning has made great strides in overcoming the limitations of classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, experimental data are often challenging to label and cannot be easily reproduced numerically. Here, we propose a deep-learning method, named LodeSTAR (Localization and detection from Symmetries, Translations And Rotations), that learns to detect microscopic objects with sub-pixel accuracy from a single unlabeled experimental image by exploiting the inherent roto-translational symmetries of this task. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy, also when analyzing challenging experimental data containing densely packed cells or noisy backgrounds. Furthermore, by exploiting additional symmetries we show that LodeSTAR can measure other properties, e.g., vertical position and polarizability in holographic microscopy.

Toward dynamic, anisotropic, high-resolution, and functional measurement in the brain extracellular space

Diffusion of substances in the brain extracellular space (ECS) is important for extrasynaptic communication, extracellular ionic homeostasis, drug delivery, and metabolic waste clearance. However, substance diffusion is largely constrained by the geometry of brain ECS and the extracellular matrix. Investigating the diffusion properties of substances not only reveals the structural information of the brain ECS but also advances the understanding of intercellular signaling of brain cells. Among different techniques for substance diffusion measurement, the optical imaging method is sensitive and straightforward for measuring the dynamics and distribution of fluorescent molecules or sensors and has been used for molecular diffusion measurement in the brain. We mainly discuss recent advances of optical imaging-enabled measurements toward dynamic, anisotropic, high-resolution, and functional aspects of the brain ECS diffusion within the last 5 to 10 years. These developments are made possible by advanced imaging, such as light-sheet microscopy and single-particle tracking in tissue, and new fluorescent biosensors for neurotransmitters. We envision future efforts to map the ECS diffusivity across the brain under healthy and diseased conditions to guide the therapeutic delivery and better understand neurochemical transmissions that are relevant to physiological signaling and functions in brain circuits.

A method for tracking the Brownian motion to estimate the size distribution of submicron particles in seawater

Because the diffusivity of particles undergoing the Brownian motion is inversely proportional to their sizes, the size distribution of submicron particles can be estimated by tracking their movement. This particle tracking analysis (PTA) has been applied in various fields, but mainly focused on resolving monodispersed particle populations and is rarely used for measuring oceanic particles that are naturally polydispersed. We demonstrated using Monte Carlo simulation that, in principle, PTA can be used to size natural, oceanic particles. We conducted a series of lab experiments using microbeads of NIST-traceable sizes to evaluate the performance of ViewSizer 3000, a PTA-based commercial instrument, and found two major uncertainties: (1) the sample volume varies with the size of particles and (2) the signal-to-noise ratio for particles of sizes < 200-250 nm was reduced and hence their concentration was underestimated with the presence of larger particles. After applying the volume correction, we found the instrument can resolve oceanic submicron particles of sizes greater than 250 nm with a mean absolute error of 3.9% in size and 38% in concentration.

Classification-based motion analysis of single-molecule trajectories using DiffusionLab

Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble techniques, which places it uniquely among techniques to study mass transport. Analysis of the trajectories obtained with single-particle tracking in inorganic porous hosts is often challenging, because trajectories are short and/or motion is heterogeneous. We present the DiffusionLab software package for motion analysis of such challenging data sets. Trajectories are first classified into populations with similar characteristics to which the motion analysis is tailored in a second step. DiffusionLab provides tools to classify trajectories based on the motion type either with machine learning or manually. It also offers quantitative mean squared displacement analysis of the trajectories. The software can compute the diffusion constant for an individual trajectory if it is sufficiently long, or the average diffusion constant for multiple shorter trajectories. We demonstrate the DiffusionLab approach via the analysis of a simulated data set with motion types frequently observed in inorganic porous hosts, such as zeolites. The software package with graphical user interface and its documentation are freely available.

A dominant-negative SOX18 mutant disrupts multiple regulatory layers essential to transcription factor activity

Few genetically dominant mutations involved in human disease have been fully explained at the molecular level. In cases where the mutant gene encodes a transcription factor, the dominant-negative mode of action of the mutant protein is particularly poorly understood. Here, we studied the genome-wide mechanism underlying a dominant-negative form of the SOX18 transcription factor (SOX18^RaOp) responsible for both the classical mouse mutant Ragged Opossum and the human genetic disorder Hypotrichosis-lymphedema-telangiectasia-renal defect syndrome. Combining three single-molecule imaging assays in living cells together with genomics and proteomics analysis, we found that SOX18^RaOp disrupts the system through an accumulation of molecular interferences which impair several functional properties of the wild-type SOX18 protein, including its target gene selection process. The dominant-negative effect is further amplified by poisoning the interactome of its wild-type counterpart, which perturbs regulatory nodes such as SOX7 and MEF2C. Our findings explain in unprecedented detail the multi-layered process that underpins the molecular aetiology of dominant-negative transcription factor function.

Three-dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions

Confined diffusion is an important model for describing the motion of biological macromolecules moving in the crowded, three-dimensional environment of the cell. In this work we build upon the technique known as sequential Monte Carlo - expectation maximization (SMC-EM) to simultaneously localize the particle and estimate the motion model parameters from single particle tracking data. We extend SMC-EM to handle the double-helix point spread function (DH-PSF) for encoding the three-dimensional position of the particle in the two-dimensional image plane of the camera. SMC-EM can handle a wide range of camera models and here we assume the data was acquired using a scientific CMOS (sCMOS) camera. The sensitivity and speed of these cameras make them well suited for SPT, though the pixel-dependent nature of the camera noise presents a challenge for analysis. We focus on the low signal setting and compare our method through simulation to more standard approaches that use the paradigm of localize-then-estimate. To localize the particle under the standard paradigm, we use both a Gaussian fit and a maximum likelihood estimator (MLE) that accounts for both the DH-PSF and the pixel-dependent noise of the camera. Model estimation is then carried out either by fitting the model to the mean squared displacement (MSD) curve, or through an optimal estimation approach. Our results indicate that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches.

Tracking single particles for hours via continuous DNA-mediated fluorophore exchange

Monitoring biomolecules in single-particle tracking experiments is typically achieved by employing fixed organic dyes or fluorescent fusion proteins linked to a target of interest. However, photobleaching typically limits observation times to merely a few seconds, restricting downstream statistical analysis and observation of rare biological events. Here, we overcome this inherent limitation via continuous fluorophore exchange using DNA-PAINT, where fluorescently-labeled oligonucleotides reversibly bind to a single-stranded DNA handle attached to the target molecule. Such versatile and facile labeling allows uninterrupted monitoring of single molecules for extended durations. We demonstrate the power of our approach by observing DNA origami on membranes for tens of minutes, providing perspectives for investigating cellular processes on physiologically relevant timescales.

The length of single-particle tracking experiments are limited due to photobleaching. Here the authors achieve long-term single-particle tracking with continuous fluorophore exchange in DNA-PAINT and use this to observe DNA origami on lipid bilayers for tens of minutes.

Detection of Anomalous Diffusion with Deep Residual Networks

Identification of the diffusion type of molecules in living cells is crucial to deduct their driving forces and hence to get insight into the characteristics of the cells. In this paper, deep residual networks have been used to classify the trajectories of molecules. We started from the well known ResNet architecture, developed for image classification, and carried out a series of numerical experiments to adapt it to detection of diffusion modes. We managed to find a model that has a better accuracy than the initial network, but contains only a small fraction of its parameters. The reduced size significantly shortened the training time of the model. Moreover, the resulting network has less tendency to overfitting and generalizes better to unseen data.

Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

The growing interest in machine learning methods has raised the need for a careful study of their application to the experimental single-particle tracking data. In this paper, we present the differences in the classification of the fractional anomalous diffusion trajectories that arise from the selection of the features used in random forest and gradient boosting algorithms. Comparing two recently used sets of human-engineered attributes with a new one, which was tailor-made for the problem, we show the importance of a thoughtful choice of the features and parameters. We also analyse the influence of alterations of synthetic training data set on the classification results. The trained classifiers are tested on real trajectories of G proteins and their receptors on a plasma membrane.

High-Precision Protein-Tracking With Interferometric Scattering Microscopy

The mobility of proteins and lipids within the cell, sculpted oftentimes by the organization of the membrane, reveals a great wealth of information on the function and interaction of these molecules as well as the membrane itself. Single particle tracking has proven to be a vital tool to study the mobility of individual molecules and unravel details of their behavior. Interferometric scattering (iSCAT) microscopy is an emerging technique well-suited for visualizing the diffusion of gold nanoparticle-labeled membrane proteins to a spatial and temporal resolution beyond the means of traditional fluorescent labels. We discuss the applicability of interferometric single particle tracking (iSPT) microscopy to investigate the minutia in the motion of a protein through measurements visualizing the mobility of the epidermal growth factor receptor in various biological scenarios on the live cell.

Revealing Plasma Membrane Nano-Domains with Diffusion Analysis Methods

Nano-domains are sub-light-diffraction-sized heterogeneous areas in the plasma membrane of cells, which are involved in cell signalling and membrane trafficking. Throughout the last thirty years, these nano-domains have been researched extensively and have been the subject of multiple theories and models: the lipid raft theory, the fence model, and the protein oligomerization theory. Strong evidence exists for all of these, and consequently they were combined into a hierarchal model. Measurements of protein and lipid diffusion coefficients and patterns have been instrumental in plasma membrane research and by extension in nano-domain research. This has led to the development of multiple methodologies that can measure diffusion and confinement parameters including single particle tracking, fluorescence correlation spectroscopy, image correlation spectroscopy and fluorescence recovery after photobleaching. Here we review the performance and strengths of these methods in the context of their use in identification and characterization of plasma membrane nano-domains.

Single-Virus Tracking: From Imaging Methodologies to Virological Applications

Uncovering the mechanisms of virus infection and assembly is crucial for preventing the spread of viruses and treating viral disease. The technique of single-virus tracking (SVT), also known as single-virus tracing, allows one to follow individual viruses at different parts of their life cycle and thereby provides dynamic insights into fundamental processes of viruses occurring in live cells. SVT is typically based on fluorescence imaging and reveals insights into previously unreported infection mechanisms. In this review article, we provide the readers a broad overview of the SVT technique. We first summarize recent advances in SVT, from the choice of fluorescent labels and labeling strategies to imaging implementation and analytical methodologies. We then describe representative applications in detail to elucidate how SVT serves as a valuable tool in virological research. Finally, we present our perspectives regarding the future possibilities and challenges of SVT.

Revealing Compartmentalized Diffusion in Living Cells with Interferometric Scattering Microscopy

The spatiotemporal organization and dynamics of the plasma membrane and its constituents are central to cellular function. Fluorescence-based single-particle tracking has emerged as a powerful approach for studying the single molecule behavior of plasma-membrane-associated events because of its excellent background suppression, at the expense of imaging speed and observation time. Here, we show that interferometric scattering microscopy combined with 40 nm gold nanoparticle labeling can be used to follow the motion of membrane proteins in the plasma membrane of live cultured mammalian cell lines and hippocampal neurons with up to 3 nm precision and 25 μs temporal resolution. The achievable spatiotemporal precision enabled us to reveal signatures of compartmentalization in neurons likely caused by the actin cytoskeleton.

A Brief History of Single-Particle Tracking of the Epidermal Growth Factor Receptor

Single-particle tracking (SPT) has been used and developed over the last 25 years as a method to investigate molecular dynamics, structure, interactions, and function in the cellular context. SPT is able to show how fast and how far individual molecules move, identify different dynamic populations, measure the duration and strength of intermolecular interactions, and map out structures on the nanoscale in cells. In combination with other techniques such as macromolecular crystallography and molecular dynamics simulation, it allows us to build models of complex structures, and develop and test hypotheses of how these complexes perform their biological roles in health as well as in disease states. Here, we use the example of the epidermal growth factor receptor (EGFR), which has been studied extensively by SPT, demonstrating how the method has been used to increase our understanding of the receptor’s organization and function, including its interaction with the plasma membrane, its activation, clustering, and oligomerization, and the role of other receptors and endocytosis. The examples shown demonstrate how SPT might be employed in the investigation of other biomolecules and systems.

Single-Nanoparticle Tracking with Angstrom Localization Precision and Microsecond Time Resolution

Gold nanoparticles (AuNPs) have been used as a contrast agent for optical imaging of various single biomolecules. Because AuNPs have high scattering efficiency without photobleaching, biomolecular dynamics have been observed with nanometer localization precision and sub-millisecond time resolution. To understand the working principle of biomolecular motors in greater detail, further improvement of the localization precision and time resolution is necessary. Here, we investigated the lower limit of localization precision achievable with AuNPs and the fundamental law, which determines the localization precision. We first used objective-lens-type total internal reflection dark-field microscopy to obtain a scattering signal from an isolated AuNP. The localization precision was inversely proportional to the square root of the photon number, which is consistent with theoretical estimation. The lower limit of precision for a 40 nm AuNP was ∼0.3 nm with 1 ms time resolution and was restricted by detector saturation. To achieve higher localization precision, we designed and constructed an annular illumination total internal reflection dark-field microscopy system with an axicon lens, which can illuminate the AuNPs at high laser intensity without damaging the objective lens. In addition, we used high image magnification to avoid detector saturation. Consequently, we achieved 1.3 Å localization precision for 40 nm AuNPs and 1.9 Å localization precision for 30 nm AuNPs at 1 ms time resolution. Furthermore, even at 33 μs time resolution, localization precisions at 5.4 Å for 40 nm AuNPs and at 1.7 nm for 30 nm AuNPs were achieved. We then observed motion of head of kinesin-1 labeled with AuNP at microsecond time resolution. Transition cycles of bound/unbound states and tethered diffusion of unbound head during stepping motion on microtubule were clearly captured with higher time resolution or smaller AuNP than those used in previous studies, indicating applicability to single-molecule imaging of biomolecular motors.

An efficient algorithm for extracting the magnitude of the measurement error for fractional dynamics

Modern live-imaging fluorescent microscopy techniques following the stochastic motion of labeled tracer particles, i.e. single particle tracking (SPT) experiments, have uncovered significant deviations from the laws of Brownian motion in a variety of biological systems. Accurately characterizing the anomalous diffusion for SPT experiments has become a central issue in biophysics. However, measurement errors raise difficulty in the analysis of single trajectories. In this paper, we introduce a novel surface calibration method based on a fractionally integrated moving average (FIMA) process as an effective tool for extracting both the magnitude of the measurement error and the anomalous exponent for autocorrelated processes of various origins. This method is developed using a toy model - fractional Brownian motion disturbed by independent Gaussian white noise - and is illustrated on both simulated and experimental biological data. We also compare this new method with the mean-squared displacement (MSD) technique, extended to capture the measurement noise in the toy model, which shows inferior results. The introduced procedure is expected to allow for more accurate analysis of fractional anomalous diffusion trajectories with measurement errors across different experimental fields and without the need for any calibration measurements.

The hitchhiker's guide to quantitative diffusion measurements

Quantitative comparison of three widely used techniques for diffusion measurements, implemented on a light sheet microscope.

Molecular membrane dynamics: Insights into synaptic function and neuropathological disease

The fluid mosaic model states that molecules in the plasma membrane can freely undergo lateral diffusion; however, in neurons and glia, specific membrane molecules are concentrated in cellular microdomains to overcome the randomizing effects of free diffusion. This specialized distribution of membrane molecules is crucial for various cell functions; one example is the accumulation of neurotransmitter receptors at the postsynaptic neuronal membrane, which enables efficient synaptic transmission. Quantum dot-single particle tracking (QD-SPT) is a super-resolution imaging technique that uses semiconductor nanocrystal quantum dots as fluorescent probes, and is a powerful tool for analyzing protein and lipid behavior in the plasma membrane. In this article, we review studies implementing QD-SPT in neuroscience research and important data gleaned using this technology. Recent QD-SPT experiments have provided critical insights into the mechanism and physiological relevance of membrane self-organization in neurons and astrocytes in the brain. The mobility of some membrane molecules may become abnormal in cellular models of epilepsy and Alzheimer's disease. Based on these findings, we propose that the behavior of membrane molecules reflects the condition of neurons in pathological disease states.

Segmentation of 3D Trajectories Acquired by TSUNAMI Microscope: An Application to EGFR Trafficking

Whereas important discoveries made by single-particle tracking have changed our view of the plasma membrane organization and motor protein dynamics in the past three decades, experimental studies of intracellular processes using single-particle tracking are rather scarce because of the lack of three-dimensional (3D) tracking capacity. In this study we use a newly developed 3D single-particle tracking method termed TSUNAMI (Tracking of Single particles Using Nonlinear And Multiplexed Illumination) to investigate epidermal growth factor receptor (EGFR) trafficking dynamics in live cells at 16/43 nm (xy/z) spatial resolution, with track duration ranging from 2 to 10 min and vertical tracking depth up to tens of microns. To analyze the long 3D trajectories generated by the TSUNAMI microscope, we developed a trajectory analysis algorithm, which reaches 81% segment classification accuracy in control experiments (termed simulated movement experiments). When analyzing 95 EGF-stimulated EGFR trajectories acquired in live skin cancer cells, we find that these trajectories can be separated into three groups-immobilization (24.2%), membrane diffusion only (51.6%), and transport from membrane to cytoplasm (24.2%). When EGFRs are membrane-bound, they show an interchange of Brownian diffusion and confined diffusion. When EGFRs are internalized, transitions from confined diffusion to directed diffusion and from directed diffusion back to confined diffusion are clearly seen. This observation agrees well with the model of clathrin-mediated endocytosis.

Single Particle Tracking: From Theory to Biophysical Applications

After three decades of developments, single particle tracking (SPT) has become a powerful tool to interrogate dynamics in a range of materials including live cells and novel catalytic supports because of its ability to reveal dynamics in the structure-function relationships underlying the heterogeneous nature of such systems. In this review, we summarize the algorithms behind, and practical applications of, SPT. We first cover the theoretical background including particle identification, localization, and trajectory reconstruction. General instrumentation and recent developments to achieve two- and three-dimensional subdiffraction localization and SPT are discussed. We then highlight some applications of SPT to study various biological and synthetic materials systems. Finally, we provide our perspective regarding several directions for future advancements in the theory and application of SPT.

Convergence of lateral dynamic measurements in the plasma membrane of live cells from single particle tracking and STED-FCS

Fluorescence correlation spectroscopy (FCS) in combination with the super-resolution imaging method STED (STED-FCS), and single-particle tracking (SPT) are able to directly probe the lateral dynamics of lipids and proteins in the plasma membrane of live cells at spatial scales much below the diffraction limit of conventional microscopy. However, a major disparity in interpretation of data from SPT and STED-FCS remains, namely the proposed existence of a very fast (unhindered) lateral diffusion coefficient, ⩾5 µm2 s-1, in the plasma membrane of live cells at very short length scales, ≈⩽ 100 nm, and time scales, ≈1-10 ms. This fast diffusion coefficient has been advocated in several high-speed SPT studies, for lipids and membrane proteins alike, but the equivalent has not been detected in STED-FCS measurements. Resolving this ambiguity is important because the assessment of membrane dynamics currently relies heavily on SPT for the determination of heterogeneous diffusion. A possible systematic error in this approach would thus have vast implications in this field. To address this, we have re-visited the analysis procedure for SPT data with an emphasis on the measurement errors and the effect that these errors have on the measurement outputs. We subsequently demonstrate that STED-FCS and SPT data, following careful consideration of the experimental errors of the SPT data, converge to a common interpretation which for the case of a diffusing phospholipid analogue in the plasma membrane of live mouse embryo fibroblasts results in an unhindered, intra-compartment, diffusion coefficient of  ≈0.7-1.0 µm2 s-1, and a compartment size of about 100-150 nm.

Imaging approaches for analysis of cholesterol distribution and dynamics in the plasma membrane

Cholesterol is an important lipid component of the plasma membrane (PM) of mammalian cells, where it is involved in control of many physiological processes, such as endocytosis, cell migration, cell signalling and surface ruffling. In an attempt to explain these functions of cholesterol, several models have been put forward about cholesterol's lateral and transbilayer organization in the PM. In this article, we review imaging techniques developed over the last two decades for assessing the distribution and dynamics of cholesterol in the PM of mammalian cells. Particular focus is on fluorescence techniques to study the lateral and inter-leaflet distribution of suitable cholesterol analogues in the PM of living cells. We describe also several methods for determining lateral cholesterol dynamics in the PM including fluorescence recovery after photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), single particle tracking (SPT) and spot variation FCS coupled to stimulated emission depletion (STED) microscopy. For proper interpretation of such measurements, we provide some background in probe photophysics and diffusion phenomena occurring in cell membranes. In particular, we show the equivalence of the reaction-diffusion approach, as used in FRAP and FCS, and continuous time random walk (CTRW) models, as often invoked in SPT studies. We also discuss mass spectrometry (MS) based imaging of cholesterol in the PM of fixed cells and compare this method with fluorescence imaging of sterols. We conclude that evidence from many experimental techniques converges towards a model of a homogeneous distribution of cholesterol with largely free and unhindered diffusion in both leaflets of the PM.

Lens-based fluorescence nanoscopy

The majority of studies of the living cell rely on capturing images using fluorescence microscopy. Unfortunately, for centuries, diffraction of light was limiting the spatial resolution in the optical microscope: structural and molecular details much finer than about half the wavelength of visible light (~200 nm) could not be visualized, imposing significant limitations on this otherwise so promising method. The surpassing of this resolution limit in far-field microscopy is currently one of the most momentous developments for studying the living cell, as the move from microscopy to super-resolution microscopy or 'nanoscopy' offers opportunities to study problems in biophysical and biomedical research at a new level of detail. This review describes the principles and modalities of present fluorescence nanoscopes, as well as their potential for biophysical and cellular experiments. All the existing nanoscopy variants separate neighboring features by transiently preparing their fluorescent molecules in states of different emission characteristics in order to make the features discernible. Usually these are fluorescent 'on' and 'off' states causing the adjacent molecules to emit sequentially in time. Each of the variants can in principle reach molecular spatial resolution and has its own advantages and disadvantages. Some require specific transitions and states that can be found only in certain fluorophore subfamilies, such as photoswitchable fluorophores, while other variants can be realized with standard fluorescent labels. Similar to conventional far-field microscopy, nanoscopy can be utilized for dynamical, multi-color and three-dimensional imaging of fixed and live cells, tissues or organisms. Lens-based fluorescence nanoscopy is poised for a high impact on future developments in the life sciences, with the potential to help solve long-standing quests in different areas of scientific research.

A review of progress in single particle tracking: from methods to biophysical insights

Optical microscopy has for centuries been a key tool to study living cells with minimum invasiveness. The advent of single molecule techniques over the past two decades has revolutionized the field of cell biology by providing a more quantitative picture of the complex and highly dynamic organization of living systems. Amongst these techniques, single particle tracking (SPT) has emerged as a powerful approach to study a variety of dynamic processes in life sciences. SPT provides access to single molecule behavior in the natural context of living cells, thereby allowing a complete statistical characterization of the system under study. In this review we describe the foundations of SPT together with novel optical implementations that nowadays allow the investigation of single molecule dynamic events with increasingly high spatiotemporal resolution using molecular densities closer to physiological expression levels. We outline some of the algorithms for the faithful reconstruction of SPT trajectories as well as data analysis, and highlight biological examples where the technique has provided novel insights into the role of diffusion regulating cellular function. The last part of the review concentrates on different theoretical models that describe anomalous transport behavior and ergodicity breaking observed from SPT studies in living cells.

Method for simultaneous localization and parameter estimation in particle tracking experiments

We present a numerical method for the simultaneous localization and parameter estimation of a fluorescent particle undergoing a discrete-time continuous-state Markov process. In particular, implementation of the method proposed in this work yields an approximation to the posterior density of the particle positions over time in addition to maximum likelihood estimates of fixed, unknown parameters. The method employs sequential Monte Carlo methods and can take into account complex, potentially nonlinear noise models, including shot noise and camera-specific readout noise, as well as a wide variety of motion models and observation models, including those representing recent engineered point spread functions. We demonstrate the technique by applying it to four scenarios, including a particle undergoing free, confined, and tethered diffusions.

Micro-object motion tracking based on the probability hypothesis density particle tracker

Tracking micro-objects in the noisy microscopy image sequences is important for the analysis of dynamic processes in biological objects. In this paper, an automated tracking framework is proposed to extract the trajectories of micro-objects. This framework uses a probability hypothesis density particle filtering (PF-PHD) tracker to implement a recursive state estimation and trajectories association. In order to increase the efficiency of this approach, an elliptical target model is presented to describe the micro-objects using shape parameters instead of point-like targets which may cause inaccurate tracking. A novel likelihood function, not only covering the spatiotemporal distance but also dealing with geometric shape function based on the Mahalanobis norm, is proposed to improve the accuracy of particle weight in the update process of the PF-PHD tracker. Using this framework, a larger number of tracks are obtained. The experiments are performed on simulated data of microtubule movements and real mouse stem cells. We compare the PF-PHD tracker with the nearest neighbor method and the multiple hypothesis tracking method. Our PF-PHD tracker can simultaneously track hundreds of micro-objects in the microscopy image sequence.

High-resolution tracking of single-molecule diffusion in membranes by confocalized and spatially differentiated fluorescence photon stream recording

The performance of a method is assessed which allows for the spatiotemporal tracking of single dye-labeled molecules during two-dimensional (2D) diffusional transits through the focal area of a modified confocal microscope. In addition to facilitating the observation of molecular diffusion paths at the shot-noise limit of bright organic emitters with spatial and temporal precisions of ∼10-20 nm and <0.5 ms, respectively, the direct access to the complete stream of detected photons is beneficial for characterizing nanoscale details such as transient pausing (binding). We discuss technical aspects of this approach, along with results from its application to measuring lipid membrane dynamics in live mammalian cells. Presented topics include a discussion of the advantages of the single-photon collection mode and instrument as well as computational considerations for the localization process. A proof-of-principle experiment shows that optical nanoscopy by stochastic single-molecule switching and position readout could be implementable in parallel with such fast molecular tracking. This would allow direct access to contextual imaging data of local cytoskeletal structural elements or localized longer-lived protein assemblies.

A critical survey of methods to detect plasma membrane rafts

The plasma membrane is still one of the enigmatic cellular structures. Although the microscopic structure is getting clearer, not much is known about the organization at the nanometre level. Experimental difficulties have precluded unambiguous approaches, making the current picture rather fuzzy. In consequence, a variety of different membrane models has been proposed over the years, on the basis of different experimental strategies. Recent data obtained via high-resolution single-molecule microscopy shed new light on the existing hypotheses. We thus think it is a good time for reviewing the consistency of the existing models with the new data. In this paper, we summarize the available models in ten propositions, each of which is discussed critically with respect to the applied technologies and the strengths and weaknesses of the approaches. Our aim is to provide the reader with a sound basis for his own assessment. We close this chapter by exposing our picture of the membrane organization at the nanoscale.

Inferring diffusion in single live cells at the single-molecule level

The movement of molecules inside living cells is a fundamental feature of biological processes. The ability to both observe and analyse the details of molecular diffusion in vivo at the single-molecule and single-cell level can add significant insight into understanding molecular architectures of diffusing molecules and the nanoscale environment in which the molecules diffuse. The tool of choice for monitoring dynamic molecular localization in live cells is fluorescence microscopy, especially so combining total internal reflection fluorescence with the use of fluorescent protein (FP) reporters in offering exceptional imaging contrast for dynamic processes in the cell membrane under relatively physiological conditions compared with competing single-molecule techniques. There exist several different complex modes of diffusion, and discriminating these from each other is challenging at the molecular level owing to underlying stochastic behaviour. Analysis is traditionally performed using mean square displacements of tracked particles; however, this generally requires more data points than is typical for single FP tracks owing to photophysical instability. Presented here is a novel approach allowing robust Bayesian ranking of diffusion processes to discriminate multiple complex modes probabilistically. It is a computational approach that biologists can use to understand single-molecule features in live cells.

Detection of diffusion anisotropy due to particle asymmetry from single-particle tracking of Brownian motion by the large-deviation principle

We show that the diffusion anisotropy due to the asymmetry of the particle can be extracted from the trajectory data without the information of the particle orientation. The subject of analysis is typical in single-particle tracking (SPT) experiments, and the analysis is based on the large-deviation principle in mathematics. We consider the model system of Langevin equations in two dimensions where a particle diffusion shows anisotropy depending on a single parameter defined by the two diffusion coefficients in the perpendicular directions of the frame fixed to the particle. We show how the large-deviation quantities depend on this parameter so that it can be used for the detection of the diffusion anisotropy. We also illustrate how the discreteness of the sampling interval in the SPT and the finiteness of the number of samples influence the results of the analysis.

Nanoscale three-dimensional single particle tracking

Single particle tracking (SPT) in biological systems is a quickly growing field. Many new technologies are being developed providing new tracking capabilities, which also lead to higher demands and expectations for SPT. Following a single biomolecule as it performs its function provides quantitative mechanistic information that cannot be obtained in classical ensemble methods. From the 3D trajectory, information is available over the diffusional behavior of the particle and precise position information can also be used to elucidate interactions of the tracked particle with its surroundings. Thus, three-dimensional (3D) SPT is a very valuable tool for investigating cellular processes. This review presents recent progress in 3D SPT, from image-based techniques toward more sophisticated feedback approaches. We focus mainly on the feedback technique known as orbital tracking. We present here a modified version of the original orbital tracking in which the intensities from two z-planes are simultaneously measured allowing a concomitant wide-field imaging. The system can track single particles with a precision down to 5 nm in the x-y plane and 7 nm in the axial direction. The capabilities of the system are demonstrated using single virus tracing to follow the infection pathway of Prototype Foamy Virus in living cells.

Multiphoton fluorescence recovery after photobleaching in bounded systems

Multiphoton fluorescence recovery after photobleaching (MP-FRAP) is a laser microscopy technique used to measure diffusion coefficients of macromolecules in biological systems. The three-dimensional resolution and superior depth penetration within scattering samples offered by MP-FRAP make it an important tool for investigating both in vitro and in vivo systems. However, biological systems frequently confine diffusion within solid barriers, and to date the effect of such barriers on the measurement of absolute diffusion coefficients via MP-FRAP has not been studied. We have used Monte Carlo simulations of diffusion and MP-FRAP to understand the effect of barriers of varying geometries and positions relative to the two-photon focal volume. Furthermore, we supply ranges of barrier positions within which MP-FRAP can confidently be employed to measure accurate diffusion coefficients. Finally, we produce two new MP-FRAP models that can produce accurate diffusion coefficients in the presence of a single plane boundary or parallel infinite plane boundaries positioned parallel to the optical axis, up to the resolution limit of the multiphoton laser scanning microscope.

Statistics of camera-based single-particle tracking

Camera-based single-particle tracking enables quantitative determination of transport properties and provides nanoscale information about material characteristics such as viscosity and elasticity. However, static localization noise and the blurring of a particle's position over camera integration times introduce artifacts into measurement results even for a particle executing simple diffusion. Common data analysis methods based on the mean-square displacement do not properly account for these effects. In this paper, we analyze the statistics of tracking data for freely diffusing particles in realistic experimental scenarios. We derive a convenient and asymptotically optimal maximum likelihood estimator for the diffusion coefficient and for the magnitude of localization noise together with the corresponding Fisher information, which bounds the performance of all unbiased estimators. We find that the effect of varying the illumination profile during the camera integration time is quantified by a motion blur coefficient, R . We also find that a double-pulse illumination sequence maximizes the information content in some common experimental scenarios. Our results provide a rigorous theoretical framework and practical experimental recipe for achieving optimal performance in camera-based single-particle tracking.

Fast molecular tracking maps nanoscale dynamics of plasma membrane lipids

We describe an optical method capable of tracking a single fluorescent molecule with a flexible choice of high spatial accuracy (approximately 10-20 nm standard deviation or approximately 20-40 nm full-width-at-half-maximum) and temporal resolution (< 1 ms). The fluorescence signal during individual passages of fluorescent molecules through a spot of excitation light allows the sequential localization and thus spatio-temporal tracking of the molecule if its fluorescence is collected on at least three separate point detectors arranged in close proximity. We show two-dimensional trajectories of individual, small organic dye labeled lipids diffusing in the plasma membrane of living cells and directly observe transient events of trapping on < 20 nm spatial scales. The trapping is cholesterol-assisted and much more pronounced for a sphingo- than for a phosphoglycero-lipid, with average trapping times of approximately 15 ms and < 4 ms, respectively. The results support previous STED nanoscopy measurements and suggest that, at least for nontreated cells, the transient interaction of a single lipid is confined to macromolecular dimensions. Our experimental approach demonstrates that fast molecular movements can be tracked with minimal invasion, which can reveal new important details of cellular nano-organization.

Morphological studies of living cells using gold nanoparticles and dark-field optical section microscopy

The morphologic changes of living cells under drug interactions were studied by using 80-nm gold nanoparticles and dark-field optical section microscopy. The gold nanoparticles were coated with poly (L-lysine), which attached to the membranes of various cells by way of electrostatic attractive force. A three-dimensional (3-D) morphological image was obtained by measuring the peak scattering intensities of gold nanoparticles at different focal planes. An algorithm for the reconstruction of 3-D cell morphology was presented. With the measured nanoparticle images and calculations, we show morphologic changes of lung cancer cells under the interaction of cytochalasin D drug at different times.

Design of quantum dot-conjugated lipids for long-term, high-speed tracking experiments on cell surfaces

The current study reports the facile design of quantum dot (QD)-conjugated lipids and their application to high-speed tracking experiments on cell surfaces. CdSe/ZnS core/shell QDs with two types of hydrophilic coatings, 2-(2-aminoethoxy)ethanol (AEE) and a 60:40 molar mixture of 1,2-dipalmitoyl- sn-glycero-3-phosphocholine and 1,2-dipalmitoyl- sn-glycero-3-phosphoethanolamine- N-[methoxy(polyethylene glycol-2000], are conjugated to sulfhydryl lipids via maleimide reactive groups on the QD surface. Prior to lipid conjugation, the colloidal stability of both types of coated QDs in aqueous solution is confirmed using fluorescence correlation spectroscopy. A sensitive assay based on single lipid tracking experiments on a planar solid-supported phospholipid bilayer is presented that establishes conditions of monovalent conjugation of QDs to lipids. The QD-lipids are then employed as single-molecule tracking probes in plasma membranes of several cell types. Initial tracking experiments at a frame rate of 30 frames/s corroborate that QD-lipids diffuse like dye-labeled lipids in the plasma membrane of COS-7, HEK-293, 3T3, and NRK cells, thus confirming monovalent labeling. Finally, QD-lipids are applied for the first time to high-speed single-molecule imaging by tracking their lateral mobility in the plasma membrane of NRK fibroblasts with up to 1000 frames/s. Our high-speed tracking data, which are in excellent agreement with previous tracking experiments that used larger (40 nm) Au labels, not only push the time resolution in long-time, continuous fluorescence-based single-molecule tracking but also show that highly photostable, photoluminescent nanoprobes of 10 nm size can be employed (AEE-coated QDs). These probes are also attractive because, unlike Au nanoparticles, they facilitate complex multicolor experiments.